@article{fdi:010091616,
  title = {{A} unified approach to publish semantic annotations of agricultural documents as knowledge graphs},
  author = {{Y}acoubi {A}yadi, {N}. and {B}ernard, {S}. and {B}ossy, {R}. and {C}ourtin, {M}. and {H}appi {H}appi, {B}ill {G}ates and {L}armande, {P}ierre and {M}ichel, {F}. and {N}{\'e}dellec, {C}. and {R}oussey, {C}. and {F}aron, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he research results presented in this paper were obtained as part of the {D}2{KAB} project ({D}ata to {K}nowledge in {A}griculture and {B}iodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the {FAIR} principles. {W}e focus on constructing domain-specific {K}nowledge {G}raphs ({KG}s) from textual data sources, using {N}atural {L}anguage {P}rocessing ({NLP}) techniques to extract and structure relevant entities. {O}ur approach is based on the formalization of a semantic data model using common linked open vocabularies such as the {W}eb {A}nnotation {O}ntology ({OA}) and the {P}rovenance {O}ntology ({PROV}). {T}he model was developed by formulating motivating scenarios and competency questions from domain experts.  {T}his model has been used to construct three different {KG}s from three distinct corpora: {P}ub{M}ed scientific publications on wheat and rice genetics and phenotyping, and {F}rench agricultural alert bulletins. {T}he named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the {W}heat {T}rait and {P}henotype {O}ntology ({WTO}), the {F}rench {C}rop {U}sage ({FCU}) thesaurus and the {P}lant {P}henological {D}escription {O}ntology ({PPDO}). {N}amed entities were extracted using different {NLP} approaches and tools. {T}he relevance of the semantic model was validated by implementing experts questions as {SPARQL} queries to be answered on the constructed {RDF} knowledge graphs. {O}ur work demonstrates how domain-specific vocabularies and systematic querying of {KG}s can reveal hidden interactions and support agronomists in navigating vast amounts of data. {T}he resources and transformation pipelines developed are publicly available in {G}it repositories.},
  keywords = {},
  booktitle = {},
  journal = {{S}mart {A}gricultural {T}echnology},
  volume = {8},
  numero = {},
  pages = {100484 [19 ]},
  ISSN = {2772-3755},
  year = {2024},
  DOI = {10.1016/j.atech.2024.100484},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091616},
}